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Explains how convolutional neural networks can analyze and process greyscale and color images by examining their pixels and applying features and pooling.
Explains the definition, use, and some real world examples of deep learning. It also explains their recurrent nature. It also illustrates the difference between feed forward and recurrent neural networks.
Gives an example of using a neural network to predict one's salary based on a number of different characteristics and by using an activation function.
Uses examples of sequential music and text generation to show how and when recurrent neural networks (specifically, long short-term memory networks) are useful.